Human behaviour plays a vital yet often under-explored role in infectious disease transmission. The COVID-19 pandemic made this apparent by revealing gaps in current epidemiological models, which largely focus on pathogen-specific parameters while underestimating the complexity of human actions and decisionmaking. This thesis investigates the impact of behaviour on the spread of respiratory pathogens across multiple scales — from small, closed populations such as care homes to the wider community — and highlights how integrating behavioural data can enhance both modelling efforts and public health policy. First, I examine how researchers and policymakers integrated behavioural assumptions during the UK’s rapid-response COVID-19 modelling efforts (Chapter 2). By analysing several of my contributed mathematical models, I show that even minor variations in adherence and compliance can profoundly alter projections, underscoring the need for better-informed, evidence-based behavioural inputs. Next, I investigate a high-resolution contact dataset from a long-term care facility (Chapter 3), focusing on staff–resident interactions. This work demonstrates the substantial heterogeneities within care home contact patterns, suggesting that more nuanced modelling can improve outbreak control strategies in these vulnerable settings. Building on these insights, I developed a stochastic network-based model based on the data (Chapter 4). Simulation results reveal how targeted surveillance and intervention policies can greatly reduce infection risk, depending on staff–resident contact patterns. I then shifted to large-scale population-level data, using repeated surveys to explore how adherence to non-pharmaceutical interventions and risk perception varied over time and across demographic groups in the UK ( Chapter 5). Finally, I broaden the scope further by examining vaccine hesitancy in a Brazilian cohort, investigating how social and demographic factors drive individual 8 decisions about vaccination (Chapter 6). Throughout, I argue that capturing the interplay between pathogen dynamics and human behaviour is crucial for more accurate epidemic models and effective policy interventions. By bridging quantitative modelling, real-world data analysis, and behavioural insights, this thesis offers a multifaceted perspective on respiratory disease control. The findings hold relevance beyond COVID-19, providing actionable strategies for future outbreaks where behaviour remains a critical, but frequently overlooked, determinant of transmission.